Page 51 - SyI Quarterly - Q3 and Q4 Edition 2023

We need to understand how CO2e (carbon dioxide equivalent) emissions are classified to understand
the impact of technology in the security industry from an environmental sustainability perspective. As
we speak, there are three defined scopes, as follows: scope 1 – direct emissions caused by an
organisation, scope 2 – indirect emissions from the purchase and use of electricity, heating, etc., and
scope 3 – indirect emissions that occur in the value chain of the organisation.

Scope 1 emissions are not relevant for Physical Security.
Direct CO2e emissions, generated by electricity or steam generation are not present in our industry.

Scope 2 emissions are already relevant for Physical Security.
Purchasing electricity for ESS is common, and we encounter it in all scenarios.

Scope 3 emissions are also relevant for Physical Security.
These emissions are represented by electronic equipment’s CO2e footprint. In this case, we can discuss
CCTV cameras or servers, access control boards or readers, or any other electronic equipment used in
an ESS.

As we can see, there is a direct correlation between ESS and CO2e emissions. Whether we are
discussing electricity consumption or equipment CO2 emissions/footprint. An increased number of
electronic devices will result in an increased CO2e footprint. If the security industry follows the same
trend as now, of replacing manned guarding with technology, it is clear that we face an increasing trend
in CO2e emissions. Moreover, Security Officers’ activities are often enhanced with different electronic or
software tools. These come with the same CO2e emissions as in the ESS case.

Also, we can take as an example Artificial Intelligence (AI), a booming and emerging technology during
the last years. AI tools are powered by Graphics Processing Units (GPUs), which are complex computer
chips able to handle billions of calculations a second. Excepting the electronic equipment’s CO2e
footprint (Scope 3), a lot of resources will be used to power and cool these processing devices,
resources which will generate a high amount of CO2e. Patterson et al. (2021) estimated that GPT-3
training, a predecessor of ChatGPT, consumed 1,287 MWh, and led to emissions of more than 550 tons
of CO2e.

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